Previous studies have demonstrated focal but limited molecular similarities between circulating tumor cells (CTCs) and biopsies using isolated genetic assays. We hypothesized that molecular similarity between CTCs and tissue exists at the single cell level when characterized by whole genome sequencing (WGS). By combining the NanoVelcro CTC Chip with laser capture microdissection (LCM), we developed a platform for single-CTC WGS. We performed this procedure on CTCs and tissue samples from a patient with advanced prostate cancer who had serial biopsies over the course of his clinical history. We achieved 30X depth and ≥ 95% coverage. Twenty-nine percent of the somatic single nucleotide variations (SSNVs) identified were founder mutations that were also identified in CTCs. In addition, 86% of the clonal mutations identified in CTCs could be traced back to either the primary or metastatic tumors. In this patient, we identified structural variations (SVs) including an intrachromosomal rearrangement in chr3 and an interchromosomal rearrangement between chr13 and chr15. These rearrangements were shared between tumor tissues and CTCs. At the same time, highly heterogeneous short structural variants were discovered in PTEN, RB1, and BRCA2 in all tumor and CTC samples. Using high-quality WGS on single-CTCs, we identified the shared genomic alterations between CTCs and tumor tissues. This approach yielded insight into the heterogeneity of the mutational landscape of SSNVs and SVs. It may be possible to use this approach to study heterogeneity and characterize the biological evolution of a cancer during the course of its natural history.

PURPOSE Photon involved-field radiotherapy (IFRT) is the standard-of-care radiotherapy for patients with leptomeningeal metastasis (LM) from solid tumors. We tested whether proton craniospinal irradiation (pCSI) encompassing the entire CNS would result in superior CNS progression-free survival (PFS) compared with IFRT. PATIENTS AND METHODS We conducted a randomized, phase II trial of pCSI versus IFRT in patients with non–small-cell lung cancer and breast cancers with LM. We enrolled patients with other solid tumors to an exploratory pCSI group. For the randomized groups, patients were assigned (2:1), stratified by histology and systemic disease status, to pCSI or IFRT. The primary end point was CNS PFS. Secondary end points included overall survival (OS) and treatment-related adverse events (TAEs). RESULTS Between April 16, 2020, and October 11, 2021, 42 and 21 patients were randomly assigned to pCSI and IFRT, respectively. At planned interim analysis, a significant benefit in CNS PFS was observed with pCSI (median 7.5 months; 95% CI, 6.6 months to not reached) compared with IFRT (2.3 months; 95% CI, 1.2 to 5.8 months; P < .001). We also observed OS benefit with pCSI (9.9 months; 95% CI, 7.5 months to not reached) versus IFRT (6.0 months; 95% CI, 3.9 months to not reached; P = .029). There was no difference in the rate of grade 3 and 4 TAEs ( P = .19). In the exploratory pCSI group, 35 patients enrolled, the median CNS PFS was 5.8 months (95% CI, 4.4 to 9.1 months) and OS was 6.6 months (95% CI, 5.4 to 11 months). CONCLUSION Compared with photon IFRT, we found pCSI improved CNS PFS and OS for patients with non–small-cell lung cancer and breast cancer with LM with no increase in serious TAEs.

The Pixel-wise Code Exposure (PCE) camera is a compressive sensing camera that has several advantages, such as low power consumption and high compression ratio. Moreover, one notable advantage is the capability to control individual pixel exposure time. Conventional approaches of using PCE cameras involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. Otherwise, conventional approaches will fail if compressive measurements are used. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done via detection using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive simulations using short wave infrared (SWIR) videos demonstrated the efficacy of our proposed approach.

Background Leptomeningeal metastasis (LM) involves CSF seeding of tumor cells. Proton craniospinal irradiation (pCSI) is potentially effective for solid tumor LM. We evaluated whether circulating tumor cells (CTCs) in the CSF (CTCCSF), blood (CTCblood), and neuroimaging correlates with outcomes after pCSI for LM. Methods We describe a single-institution consecutive case series of 58 patients treated with pCSI for LM. Pre-pCSI CTCs, the change in CTC post-pCSI (ΔCTC), and MRIs were examined. Central nervous system progression free survival (CNS-PFS) and overall survival (OS) from pCSI were determined using Kaplan Meier analysis, Cox proportional-hazards regression, time-dependent ROC analysis, and joint modeling of time-varying effects and survival outcomes. Results The median CNS-PFS and OS were 6 months (IQR:4-9) and 8 months (IQR:5-13), respectively. Pre-pCSI CTCCSF&lt;53/3mL was associated with improved CNS-PFS (12.0 vs 6.0 months, p&lt;0.01). Parenchymal brain metastases (n=34, 59%) on pre-pCSI MRI showed worse OS (7.0 vs 13 months, p=0.01). Through joint modeling, CTCCSF was significantly prognostic of CNS-PFS (p&lt;0.01) and OS (p&lt;0.01). A ΔCTC-CSF≥37 cells/3mL, the median ΔCTC-CSF at nadir, showed improved CNS-PFS (8.0 vs 5.0 months, p=0.02) and further stratified patients into favorable and unfavorable subgroups (CNS-PFS 8.0 vs 4.0 months, p&lt;0.01). No associations with CTCblood were found. Conclusion We found the best survival observed in patients with low pre-pCSI CTCCSF and intermediate outcomes for patients with high pre-pCSI CTCCSF but large ΔCTC-CSF. These results favor additional studies incorporating pCSI and CTCCSF measurement earlier in the LM treatment paradigm.

Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.